Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 민경한 | - |
dc.date.accessioned | 2020-09-15T05:56:19Z | - |
dc.date.available | 2020-09-15T05:56:19Z | - |
dc.date.issued | 2019-09 | - |
dc.identifier.citation | World Electric Vehicle Journal, v. 10, no. 3, article no. 57 | en_US |
dc.identifier.issn | 2032-6653 | - |
dc.identifier.uri | https://www.mdpi.com/2032-6653/10/3/57 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/153927 | - |
dc.description.abstract | A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking. © 2019 by the authors. | en_US |
dc.description.sponsorship | This work was financially supported by the BK21 plus program (22A20130000045) under the Ministry of Education, Republic of Korea, the Industrial Strategy Technology Development Program (No. 10039673, 10060068, 10079961), the International Collaborative Research and Development Program (N0001992) under the Ministry of Trade, Industry and Energy (MOTIE Korea), and National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2011-0017495). The experiment vehicle was supported by the Hyundai motor company. | en_US |
dc.language.iso | en | en_US |
dc.publisher | The World Electric Vehicle Association (WEVA) | en_US |
dc.subject | Autonomous deceleration control | en_US |
dc.subject | Electric vehicle | en_US |
dc.subject | Advanced driver assistance system | en_US |
dc.subject | Deceleration planning | en_US |
dc.subject | Reinforcement learning | en_US |
dc.subject | Driver characteristics | en_US |
dc.title | Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/wevj10030057 | - |
dc.relation.page | 1-18 | - |
dc.relation.journal | World Electric Vehicle Journal | - |
dc.contributor.googleauthor | Min, Kyunghan | - |
dc.contributor.googleauthor | Sim, Gyubin | - |
dc.contributor.googleauthor | Ahn, Seongju | - |
dc.contributor.googleauthor | Park, Inseok | - |
dc.contributor.googleauthor | Yoo, Seungjae | - |
dc.contributor.googleauthor | Youn, Jeamyoung | - |
dc.relation.code | 2019033228 | - |
dc.sector.campus | S | - |
dc.sector.daehak | RESEARCH INSTITUTE[S] | - |
dc.sector.department | AUTOMOTIVE RESEARCH CENTER AT HANYANG UNIVERSITY | - |
dc.identifier.pid | sturm | - |
dc.identifier.orcid | https://orcid.org/0000-0003-4275-476X | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.